Iris deblurring method based on global and local iris image statistics

ABSTRACT

A method of identifying a living being includes using a camera to capture a blurred visual image of an iris of the living being. The blurred visual image is digitally unblurred based on a distribution of eye image gradients in an empirically-collected sample of eye images and characteristics of pupil region. The unblurred image is processed to determine an identity of the living being.

RELATED APPLICATIONS

This application is a continuation of, and claims the benefit of, U.S.application Ser. No. 12/367,038 filed Feb. 6, 2009, which is herebyincorporated by reference herein.

BACKGROUND

1. Field of the Invention

The present invention relates to apparatuses and methods for identifyingpersonnel and, more particularly, to apparatuses and methods foridentifying personnel based on visual characteristics of the irises oftheir eyes.

2. Description of the Related Art

Iris recognition, or “iris capture” is a method of biometric personalidentification that uses pattern recognition algorithms based on imagesof at least one of the irises of an individual's eyes. Iris recognitionuses camera technology to produce images of the details of the iris.These images are converted into digital templates and providemathematical representations of the iris that are used to identifyindividuals.

For most iris capturing systems, captured iris images easily blur whenthe user is out of the depth of field (DOF) of the camera, or when he ismoving. The common solution is to have the user try the system and havethe system read his iris again, as the quality of the previouslycaptured blurred iris images is not good enough for recognition.

What is neither disclosed nor suggested in the art is an iris capturesystem that can correct and/or compensate for blurring of capturedimages such that the need to repeat the capturing of the iris image dueto blurring is reduced.

SUMMARY

The present invention provides a novel iris deblurring algorithm thatcan be used to improve the robustness or nonintrusiveness of all iriscapturing systems. Unlike other iris deblurring algorithms, the domainknowledge (or prior knowledge) inherent in iris images is utilized. Thisdomain knowledge may be in the form of statistics related to global irisimages (i.e., images of the iris and surrounding area, perhaps includingsome skin), or statistics related to characteristics of local pupils orhighlights (i.e., the portion of the pupil reflecting the highestbrightness of light to the camera), for example.

In one embodiment, the present invention comprises a method ofidentifying a living being, including using a camera to capture ablurred visual image of an iris of the living being. The blurred visualimage is digitally unblurred based on a distribution of eye imagegradients in an empirically-collected sample of eye images. Theunblurred image is processed to determine an identity of the livingbeing.

In another embodiment, the present invention comprises a method ofidentifying a living being, including using a camera to capture ablurred visual image of an iris of the living being. The blurred visualimage is digitally unblurred based on local color value statisticsderived from measurements of a population of pupils, and global colorvalue statistics derived from measurements of a population of eyeregions. The unblurred image is processed to determine an identity ofthe living being.

In yet another embodiment, the present invention comprises a method ofidentifying a living being, including capturing a blurred visual imageof an iris of the living being. The blurred visual image is digitallyunblurred based on local highlight color value statistics derived frommeasurements of highlight regions of a population of pupils; localnon-highlight color value statistics derived from measurements ofnon-highlight regions of a population of pupils; and global color valuestatistics derived from measurements of a population of eye regions. Theunblurred image is processed to determine an identity of the livingbeing.

An advantage of the present invention is that it can correct blurredimages such that they are useful in iris recognition.

BRIEF DESCRIPTION OF THE DRAWINGS

The above mentioned and other features and objects of this invention,and the manner of attaining them, will become more apparent and theinvention itself will be better understood by reference to the followingdescription of an embodiment of the invention taken in conjunction withthe accompanying drawings, wherein:

FIG. 1 is a block diagram of one embodiment of an iris capture systemaccording to one embodiment of the invention;

FIG. 2 is an operational block diagram of the iris capture system ofFIG. 1;

FIG. 3 is an example of a fitted curve for the measured focus positionsof the camera of the system of FIG. 1 as a function of the depth betweenthe camera lens and the object.

FIG. 4 a illustrates examples of plots of the standard deviation of theblur kernel Gaussian distribution as a function of the focus position ofthe camera of the system of FIG. 1 for various distances between thecamera and the iris according to one embodiment of a method of thepresent invention for visually recognizing an iris.

FIG. 4 b is the plot of FIG. 4 a corresponding to a distance of 3.30meters between the camera and the iris.

FIG. 4 c is the plot of FIG. 4 a corresponding to a distance of 2.97meters between the camera and the iris.

FIG. 4 d is the plot of FIG. 4 a corresponding to a distance of 2.56meters between the camera and the iris.

FIG. 4 e is the plot of FIG. 4 a corresponding to a distance of 2.00meters between the camera and the iris.

FIG. 4 f is the plot of FIG. 4 a corresponding to a distance of 1.58meters between the camera and the iris.

FIG. 4 g is the plot of FIG. 4 a corresponding to a distance of 1.43meters between the camera and the iris.

FIG. 4 h is a plot illustrating how a standard deviation defining a blurkernel distribution appropriate for deblurring may be calculatedaccording to one embodiment of a method of the present invention.

FIG. 5 is a plot of the distributions of image gradients of randomnatural images and of global iris images.

Corresponding reference characters indicate corresponding partsthroughout the several views. Although the drawings representembodiments of the present invention, the drawings are not necessarilyto scale and certain features may be exaggerated in order to betterillustrate and explain the present invention. Although theexemplification set out herein illustrates embodiments of the invention,in several forms, the embodiments disclosed below are not intended to beexhaustive or to be construed as limiting the scope of the invention tothe precise forms disclosed.

DETAILED DESCRIPTION

The embodiments hereinafter disclosed are not intended to be exhaustiveor limit the invention to the precise forms disclosed in the followingdescription. Rather the embodiments are chosen and described so thatothers skilled in the art may utilize its teachings.

Turning now to the drawings, and particularly to FIG. 1, there is shownone embodiment of an iris capture system 20 of the present inventionincluding an NFOV NIR camera 22 with adjustable focus, an NIRilluminator 24, and a depth sensor 26 all in electronic communicationwith a central processor 28. System 20 may capture images of, and detectthe positions of, moving subjects such as a human being 30 or a humanbeing 32 when he approaches a doorway at which camera 22, illuminator 24and sensor 26 are mounted, such as in a direction indicated by arrow 36.Camera 22 may be installed with a mounting height H and tilt angle αsuch that a standoff distance 38 for the user is approximately between1.5 meters and 3.5 meters and the captured iris diameter is above 150pixels. In one embodiment, height H is about 250 centimeters. The widthof a capture volume 40 may be on the order of 20 centimeters. In theembodiment illustrated in FIG. 1, a width 42 of capture volume 40 wherethe image and shape of the taller person 30 are captured is about 17centimeters, and a width 44 of capture volume 40 where the image andshape of the shorter person 32 are captured is about 30 centimeters.There are many devices known for measuring depth information, such asstereo cameras, time-of-flight sensors, and structure lights.

In embodiments in which NFOV camera 22 does not have panning and tiltingcapabilities, the human being whose image and shape are being capturedneeds to look at camera 22 while approaching the doorway. The iriscapture may be triggered at different standoff distances for users withdifferent heights.

Depth sensor 26 may be installed at various positions and orientations.Depth sensor 26 may be positioned very close to NFOV camera 22 to allowfor a more compact design. NIR illuminator 24 can be placed at anylocation so long as it illuminates capture volume 40.

System 20 can be applied to other possible settings in which depthsensor 26 is used. For example, camera 22 may be in the form of a highspeed, high performance video camera. Alternatively, camera 22 may havea fixed focus or adjustable focus based on the distance between thecamera and the user. It is also possible for camera 22 to includepan-tilt capabilities in order to further enlarge the capture volume.

An operational block diagram of system 20 is illustrated in FIG. 2. Thethree-dimensional information measured by depth sensor 26 may be used invarious ways within system 20. First, face detection and tracking 46 maybe performed on the up-sampled intensity images 48 captured by depthsensor 26. The three-dimensional position of the eyes may then beestimated from an upper portion of the detected face depth maps. Thenext eye location for the moving subject may be predicted accurately inreal time. For example, time rates of change of the three-dimensionalposition of the eyes may be extrapolated to predict future eyelocations. Second, the three-dimensional position may be used todetermine whether eyes are within the field of view and whether thestand-off distance is within the depth of field. If these two conditionsare satisfied, the NFOV camera may be instructed to perform imagecapturing, as at 50. Third, the depth information may be used todynamically control the focus position of the lens of NFOV camera 22.Finally, the depth information can be used to estimate the blur kernel52 for iris deblurring, as at 53. The deblurring may be useful in aniris recognition algorithm 55. More accurate depth information could beused to predict the speed and future positions of the human being sothat the real or desired focus position can be estimated more accuratelyeven when the system delay exists. The real or desired focus positionmay represent the focus position that is ideal for the future estimatedposition of the human being.

Calibration between NFOV camera 22 and depth sensor 26 may be performed,as at 54. In one embodiment, depth sensor 26 could be a TOF sensor. Manyexisting TOF sensors contain systematic depth bias from the demodulationof correlation function and incident lights, and so calibration, orso-called “precalibration,” of the TOF sensor may obtain a better depthmeasurement. In a first step of a novel calibration method of thepresent invention, a large planar board may be positioned at differentdepths and with different orientations. A robust plane fitting may thenbe applied for the planar board at each position. The depth bias may beestimated by computing the difference between measured depth and thefitted plane. After the calibration of TOF sensor 26, the depthuncertainty may be greatly reduced, especially the depth uncertaintybetween 1.3 and 2 meters. In order to transform the depth in thecoordinate system of TOF sensor 26 to that of NFOV camera 22, a fullsystem calibration may be performed. The NFOV camera with a telephotolens may be approximated as an affine camera. A planar checkerboardpattern is captured at different depths. As the correspondences betweenthe two-dimensional points x from NFOV camera 22 and three-dimensionalpoints X from TOF sensor 26 are known, the projection matrix P can becomputed by minimizing the re-projection errors. The intrinsic andextrinsic matrices may be obtained by RQ decomposition of P.

Blur kernel estimation step 52 for iris deblurring is optional. As longas the iris deblurring algorithm needs to use the accurate depthinformation, the depth information provided by TOF sensor 26 may besufficient. When depth information is not available in capturingsystems, some statistics of the captured image (e.g., focus scores) maybe used to estimate blur kernel.

Image blur may be modeled as a convolution process:I=L

h+n  (1)where I, L, h, and n represent the blurred image; un-blurred image;point spread function (PSF) or blur kernel; and additive noise,respectively. For defocus blur, the PSF h depends on the circle ofconfusion R. For cameras with adjustable focus, R is a function of twoparameters based on the typical pin-hole camera model. The twoparameters are the distance from the object to the lens d and thedistance between the lens and the image plane s,

$\begin{matrix}{R = {\frac{Ds}{2}{{\frac{1}{f} - \frac{1}{d} - \frac{1}{s}}}}} & (2)\end{matrix}$where D is the radius of the lens, and f is the focal length of thelens. For cameras with fixed focus s, R is determined only by d.

The PSF h for the defocus blur may be modeled as a Gaussian kernel,

$\begin{matrix}{h = {\frac{1}{2{\pi\sigma}_{h}^{2}}{{\mathbb{e}}^{- \frac{x^{2} + y^{2}}{2\sigma_{h}^{2}}}.}}} & (3)\end{matrix}$Because the captured eye region is usually parallel to the image plane,the PSF h may be shift-invariant.

The blur kernel estimation method of the present invention will now bedescribed with the assumption in place that the depth difference ismeasured. When the fixed focus cameras are used, it is relatively simpleto estimate the kernel. The kernel estimation method of the presentinvention may deal with the more general case, i.e., cameras withadjustable focus. As mentioned above, the depth difference may be mainlycaused by the system delay when a subject is moving.

As the lens focus position p_(f) is proportional to the distance betweenthe lens and image plane s, when the circle of confusion R is smallenough, the relationship between the in-focus position of lens p_(f) andd may be derived based on Equation (2),

$\begin{matrix}{p_{f} = {\frac{d}{{k_{1}d} + k_{2}}.}} & (4)\end{matrix}$

After measuring focus positions from in-focus images at differentdepths, k₁ and k₂ can be easily estimated by curve fitting usingEquation (4). FIG. 3 shows an example of a fitted curve for the measuredfocus positions and depths.

As the standard deviation of the blur kernel Gaussian distribution σ_(h)is proportional to R and s is proportional to p_(f), when d is fixed,the relationship between σ_(h) and p_(f) may be derived, based onEquation (2),σ_(h) =|k ₃ p _(f) +k ₄|.  (5)

Although the parameters k₁, k₂, k₃ and k₄ are characteristics of thecamera system, they have no obvious physical meaning or representation.The standard deviation σ_(h), which defines the blur kernel Gaussiandistribution, cannot be measured directly. Thus, the following novelalgorithm of the present invention may estimate σ_(h) and then learn k₃and k₄ accordingly.

In a first step of the algorithm, in-focus and defocused checkerboardimages are captured under different depths and different focuspositions. As in-focus and defocused images are known, only σ_(h) isunknown. The standard deviation σ_(h) is estimated by argmin_(σ) _(h)||I−L

h||₂ ². The subscript 2 in the formula denotes a Euclidean Norm or aL2-Norm.

In a next step, k₃ and k₄ are estimated byargmin_(k3,k4)||k₃p_(f)+k₄−σ_(h)||₂ ². FIGS. 4 a-g show examples of thefitting results for p_(f) and σ_(h) based on Equation (5). FIGS. 4 a-gare plots of the focus position of camera 22 versus a standard deviationof the blur kernel distribution for six different distances betweencamera 22 and the subject iris. The plot for each of the six distancesis V-shaped, with the origin of the “V” being at the in-focus positioncorresponding to that distance. The parameter k₃ may represent the slopeof a corresponding V-shaped plot in FIGS. 4 a-g; and parameter k₄ mayrepresent the y-intercept of the corresponding V-shaped plot. V-shapedplot 60 corresponds to a distance of about 3.30 meters; V-shaped plot 62corresponds to a distance of about 2.97 meters; V-shaped plot 64corresponds to a distance of about 2.56 meters; V-shaped plot 66corresponds to a distance of about 2.00 meters; V-shaped plot 68corresponds to a distance of about 1.58 meters; and V-shaped plot 70corresponds to a distance of about 1.43 meters.

Each of the circles in FIGS. 4 a-g represents an empirically-collecteddata point. The data points at the top (standard deviation=20) of FIGS.4 a-g are the images that are severely blurred. It may not be feasibleto recover these kinds of severely blurred images in practice even witha large kernel size. Hence, these severely blurred images are treated asoutliers and are not included in the estimation.

Based on FIGS. 3 and 4 a-g, it can be concluded that the modelsdescribed in Equations (4) and (5) may be used for real camera systemseven though the derivation of Equations (4) and (5) is based on thetraditional pin-hole camera model. A practical use of the plots of FIGS.4 a-g is to estimate the blur kernel when the subject is moving.

When a user enters the field of view of the capturing system, thethree-dimensional position of the user's eyes after the system delay maybe predicted. When the predicted eye position satisfies the triggeringcondition, the predicted in-focus position {tilde over (p)}_(f) iscomputed using Equation (4) and the image is produced at this position.The correct (i.e., actual) depth at the time of image capture (after thesystem delay) is measured, and the correct or ideal in-focus position p_(f) corresponding to the actual depth measurement is computed. Forexample, assuming the correct or ideal in-focus position p _(f) is 15(as shown as the origin of the V-shaped plot in FIG. 4 h) for an actual,measured depth, a new model can be interpolated (i.e., Equation (5) withdifferent values for k₃ and k₄. The new model is illustrated as thedashed V-shaped plot originating at focus position 15 in FIG. 4 h.Assuming the predicted in-focus position {tilde over (p)}_(f) that wasactually used to produce the iris image is 13.5, as indicated by therectangle at 13.5 in FIG. 4 h, the standard deviation σ_(h) that definesthe blur kernel distribution appropriate for use in deblurring is shownto be approximately 8 in FIG. 4 h. The standard deviation σ_(h) may becomputed by taking the predicted focus position of 13.5 that wasactually used to produce the image, and plugging that value of 13.5 intoEquation (5) along with the values of k₃ and k₄ that correspond to theactual depth measurement (i.e., the actual depth measurement thatcorresponds to an ideal focus position of 15).

The above-described calculation of the blur kernel Gaussian distributionmay be used to unblur a captured blurred image as described in detailbelow. Particularly, the process of image deblurring may be formulatedin the Bayesian framework by Bayes' theorem,P(L|σ _(h) ,I)∝P(I|L,σ _(h))P(L)

where P(I|L,σ_(h)) is the likelihood that L is the clear image given ablur kernel defined by a Gaussian distribution that is, in turn, definedby a standard deviation σ_(h). P(L) represents the prior on theun-blurred image L. A prior probability, or a “prior,” is a marginalprobability, interpreted as what is known about a variable in theabsence of some evidence. The posterior probability is then theconditional probability of the variable taking the evidence intoaccount. The posterior probability may be computed from the prior andthe likelihood function via Bayes' theorem.

Different priors chosen in this framework may lead to differentdeblurring algorithms with different performances. The novel irisdeblurring algorithm of the present invention may be applied in any iriscapture system to handle defocus blur. The prior on the un-blurred imageL may depend upon three prior components that are based on global andlocal iris image statistics:P(L)=P _(g)(L)P _(p)(L)P _(s)(L).The first prior P_(g)(L) may be computed from an empirically-determinedglobal distribution of the iris image gradients; P_(p)(L) may becomputed based on characteristics of dark pupil region; and P_(s)(L) maybe computed from the pupil saturation region (i.e., the highlight regionof the pupil that is saturated with intensity values of highbrightness). For general image deblurring, the global distribution ofiris image gradients may be approximated by a mixture of Gaussiandistributions, exponential functions, and piece-wise continuousfunctions. Mixture Gaussian distributions are described in “Removingcamera shake from a single photograph”, R. Fergus, B. Singh, A.Hertzmann, S. T. Roweis, and W. T. Freeman, ACM Transactions onGraphics, 2006; exponential functions are described in “Image and depthfrom a conventional camera with a coded aperture”, A. Levin, R. Fergus,F. Durand, and W. T. Freeman, ACM Transactions on Graphics, 2007; andpiece-wise continuous functions are described in “High-quality motiondeblurring from a single image”, Q. Shan, J. Jia, and A. Agarwala, InSIGGRAPH, 2008, each of which is incorporated by reference herein in itsentirety.

Because the application domain is iris images rather than naturalimages, according to one embodiment of the present invention, the globaldistribution may be computed from iris images only. As illustrated inFIG. 5, the distribution of general natural images (i.e., any imagesfound in nature, such as sky, water, landscape) has a greateruncertainty than the distribution of global iris images. The presentinvention takes advantage of the tight range of the global iris imagestatistics.

As a result of the tighter iris image statistics, the distribution ofiris image gradients is a stronger prior. A two-piecewise quadraticfunction (i.e., a piecewise quadratic function having two separate,continuous portions) may be used to approximate the distribution so thatthe optimization based on this Bayesian problem becomes simpler and moreefficient. A general form of the two-piecewise quadratic function maybe:

${P_{g}(L)} \propto \left\{ \begin{matrix}{{\prod\limits_{i}\;{\mathbb{e}}^{{a_{1}{({\partial L_{i}})}}^{2} + b_{1}}},{{{\partial L_{i}}} \leq k}} \\{{\prod\limits_{i}\;{\mathbb{e}}^{{a_{2}{({\partial L_{i}})}}^{2} + b_{2}}},{{{\partial L_{i}}} > k}}\end{matrix} \right.$where ∂ L_(i) is the gradient for a pixel and k is the threshold betweentwo functions. Such a two-piecewise quadratic function may berepresented by the fitted curve in FIG. 5, wherein the threshold k is atthe transitions between the low frequency and high frequency regions.

The second P_(p)(L) and third P_(S)(L) priors may be computed from thelocal pupil region because the dark pupil region is likely to be smoothas compared with the nearby iris patterns, and the highlight region islikely saturated. Therefore, these two priors may be particularly usefulin recovering nearby iris patterns. As the smooth pupil region tends tohave small gradients that are not sensitive to the defocus blur, and thesaturated highlight region tends to contain the highest intensity, thetwo priors may be computed as following:

${P_{p}(L)} \propto {\prod\limits_{i \in \Omega_{1}}\;{N\left( {{{{\partial L_{i}} - {\partial I_{i}}}❘0},\sigma_{p}} \right)}}$${{P_{s}(L)} \propto {\prod\limits_{i \in \Omega_{2}}\;{N\left( {{{L_{i} - 255}❘0},\sigma_{s}} \right)}}},$where Ω₁ is the dark pupil region (i.e., excluding the highlightregion), and Ω₂ is the saturated highlight region within the pupil. Thedark pupil region and the saturated highlight region within the pupilcan be detected by image processing techniques, such as thresholding,erosion and dilation. The 255 term in the P_(s)(L) formula representsthe highest (i.e., whitest) color value on a scale of 0 to 255.

Putting all of these priors together, this iris deblurring problem maybe solved by minimizing an energy function E in the following quadraticform:

E ∝ I − L ⊗ h² + λ₁(a₁(∂L)² + b₁ ⋅ M₁ + a₂(∂L)² + b₂ ⋅ M₂) + λ₂(∂L − ∂I² ⋅ M₃ + L − 255² ⋅ M₄),where M₁, M₂, M₃, and M₄ are masks of low-frequency region,high-frequency region, dark pupil region, and highlight region in thepupil; I is the known blurred image captured by the camera lens; h isthe blur kernel, which may be estimated as discussed in detail above;and L is the clear image that is being determined. Thus, given knownvalues for the blurred image I and the blur kernel h, an image L may bedetermined that minimizes E, and this image L may be used as arepresentation of a clear, unblurred version of the produced blurredimage I.

The deblur kernel h can be estimated based on the depth information orfocus scores. If the blur kernel is not known, it is possible to add aGaussian prior in place of the blur kernel in order to convert thenon-blind deconvolution into a blind one, which still can be solved bythe optimization framework.

While this invention has been described as having an exemplary design,the present invention may be further modified within the spirit andscope of this disclosure. This application is therefore intended tocover any variations, uses, or adaptations of the invention using itsgeneral principles. Further, this application is intended to cover suchdepartures from the present disclosure as come within known or customarypractice in the art to which this invention pertains.

What is claimed is:
 1. A method of identifying a living being,comprising the steps of: using a camera to capture a blurred visualimage of an iris of the living being; digitally unblurring the blurredvisual image based on a distribution of eye image gradients in anempirically-collected sample of eye images and distributions fromcharacteristics of local regions; and processing the unblurred image todetermine an identity of the living being.
 2. The method of claim 1comprising the further step of computing the distributions fromcharacteristics of local regions from iris images only.
 3. The method ofclaim 1 comprising the further step of approximating the distribution ofeye image gradients as a quadratic function and/or a piecewise function.4. The method of claim 1 comprising the further step of forming thedistribution of eye image gradients from measurements of a population ofeye regions.
 5. The method of claim 1 comprising the further steps of:forming the distributions from characteristics of local regions fromcharacteristics of intensity value of pupil region; and forming thedistribution of eye image gradients from measurements of a population ofeye regions.
 6. The method of claim 5 wherein the distributions fromcharacteristics of intensity value of pupil region include: distributionfrom local highlight intensity value of pupil region; and distributionfrom local non-highlight intensity value of pupil region.
 7. The methodof claim 6 comprising the further steps of: detecting the highlightregions and the non-highlight regions around pupil region in a capturedimage; deriving the distribution of the local highlight intensity valuefrom the portion identified as corresponding to the highlight regions;and deriving the distribution of the local non-highlight intensity valuefrom the portion identified as corresponding to the non-highlightregions.
 8. The method of claim 1 wherein the distribution comprises adistribution of iris image gradients.
 9. A method of identifying aliving being, comprising the steps of: using a camera to capture ablurred visual image of an iris of the living being; digitallyunblurring the blurred visual image based on: distributions fromintensity value of pupil region; and distribution from global intensityvalue statistics derived from measurements of a population of eyeregions; and processing the unblurred image to determine an identity ofthe living being.
 10. The method of claim 9 comprising the further stepof modeling the global intensity value statistics as a quadraticfunction and/or a piecewise function.
 11. The method of claim 9 whereindistributions from characteristics of intensity value of pupil regioninclude: distribution from local highlight intensity value of pupilregion; and/or distribution from local non-highlight intensity value ofpupil region.
 12. The method of claim 11 comprising the further stepsof: detecting the highlight regions and the non-highlight regions aroundpupil region in a captured image; deriving the distribution of the localhighlight intensity value from the portion identified as correspondingto the highlight regions; and deriving the distribution of the localnon-highlight highlight intensity value from the portion identified ascorresponding to the non-highlight regions.
 13. The method of claim 9wherein the processing step includes matching the unblurred image to astored image of an iris of the living being.
 14. The method of claim 9comprising the further step of estimating a blur kernel, the unblurringstep including digitally unblurring the blurred visual image based onthe estimated blur kernel.
 15. A method of identifying a living being,comprising the steps of: capturing a blurred visual image of an iris ofthe living being; digitally unblurring the blurred visual image basedon: distribution from local highlight intensity value of pupil region;distribution from local non-highlight intensity value of pupil region;and distribution of global intensity value statistics derived frommeasurements of a population of eye regions; and processing theunblurred image to determine an identity of the living being.
 16. Themethod of claim 15 comprising the further step of modeling the globalintensity value statistics as a quadratic function and/or a piecewisefunction.
 17. The method of claim 16 wherein the quadratic piecewisefunction consists of two separate continuous functions.
 18. The methodof claim 15 comprising the further steps of: detecting the highlightregions around pupil region in a captured image; and deriving thedistribution of the local highlight intensity value from the portionidentified as corresponding to the highlight regions.
 19. The method ofclaim 15 comprising the further steps of: detecting the non-highlightregions around pupil region in a captured image; and deriving thedistribution of the local non-highlight highlight intensity value fromthe portion identified as corresponding to the non-highlight regions.20. The method of claim 15 wherein the processing step includes matchingthe unblurred image to a stored image of an iris of the living being,the method comprising the further step of estimating a blur kernel, theunblurring step including digitally unblurring the blurred visual imagebased on the estimated blur kernel.